环绕智能环境下自适应服务选择方法研究

Adaptive Services Selection Method Research in AmI Environment

由于Web Services数量的快速增加以及用户偏好的不同,在环绕智能环境下自适应地为用户选择合适服务是比较困难的.论文提出一种利用对服务的评价信息来获取用户偏好的学习机制.在此基础上,论文给出了基于信誉度和多属性决策的动态自适应服务选择算法.该算法首先利用学习到的偏好信息来产生当前用户的服务请求偏好值,然后利用加权欧氏距离及信誉度机制来选择最合适的服务推荐给用户.最后通过原型系统测试验证了算法的有效性和可用性.

Due to the fast increase of the available web services and the difference of the users′ preferences,it is difficult to select a suitable service for a user in the AmI environment in adaptive way.This paper puts forward a learning mechanism to obtain the users′ preferences by using their evaluation of the services.On this basis,it proposes a dynamic adaptive service selection algorithm based on the reputation and the multi-attribute decision making theory.The algorithm,firstly,can generate the factor of the user′s preference by means of the obtained preference information.Then it selects the most suitable service to recommend to the user with the help of the services′ reputation and the weighted Euclidean distance between the candidate services and the user′s preferences.At last,a prototype system is built to verify the effectiveness and the availability of the proposed algorithm.